Affiliations: School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Correspondence:
[*]
Corresponding author. Jinkun Sun, School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China. E-mail: sunjinkunj@sina.com.
Abstract: In order to improve the accuracy and efficiency of power instability prediction for wind turbines, a power instability prediction method for wind turbines based on fuzzy decision tree is proposed. According to the variation curve of maximum output power, the maximum power of wind turbine is searched and controlled by climbing hill. The maximum power of wind turbine is tracked by the control results. The power fluctuation periodicity rule is obtained based on the fuzzy decision tree. The power instability prediction model of wind turbine is established to realize the power instability prediction. The experimental results show that the proposed method has high effectiveness, the highest prediction accuracy can reach 95.53%, and the maximum prediction time is only 1.8 s, which fully shows that the proposed method is more suitable for the power instability prediction of wind turbines.
